Basic Terminologies For Data Structure

Prashant | Mon, 24 Aug, 2020 | 96

In order to learn the data structure pretty well, we need to know the basic terminologies that we are going to use throughout the sessions.

If you are not aware of basic things, I warn you to stop here and start learning basic input-output thing in any programming language, why I am saying this is because it is very important to know few things before directly jumping to data structure.

So here are few very easy things you should know.

Variables

Before going to the definition of variables, let us relate them to old mathematical equations. All of us have solved many mathematical equations since childhood. As an example, consider the below equation:

x+y=1

Basically we do not need to worry about what variable is, Just know "Variable" is the name given to any value.

Data types

Data type is nothing but a representation of a variable, Previously we learned that any name was given to certain value to termed as Variables, Well all those variables are of a particular type.

In computer science and computer programming, a data type or simply type is an attribute of data that tells the compiler or interpreter how the programmer intends to use the data. Most programming languages support basic data types of integer numbers, floating-point numbers, characters, and Booleans.

Data Structure

Based on the discussion above, We know what is data and variables, Once we have some data in variables, we need to superpower to manipulate and use this data efficiently. So here is our superhero "Data Structure", by the help of DS we can easily manipulate and organize data.

Depending on the organization of the elements, data structures are classified into two types:

  • Linear data structures: Elements are accessed in sequential order but it is not compulsory to store all elements sequentially. Examples: Linked Lists, Stacks, and Queues.
  •  Non – linear data structures: Elements of this data structure are stored/accessed in a non-linear order. Examples: Trees and graphs.

 

 

 

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